diff --git a/DESCRIPTION b/DESCRIPTION
index 300c7515..2139d2e2 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -50,6 +50,9 @@ Suggests:
     rpart,
     testthat (>= 3.0.0),
     xgboost
+Remotes:
+    mlr-org/mlr3,
+    mlr-org/bbotk
 VignetteBuilder:
     knitr
 Config/testthat/edition: 3
diff --git a/R/ArchiveAsyncTuning.R b/R/ArchiveAsyncTuning.R
index ad0e208d..2e272a27 100644
--- a/R/ArchiveAsyncTuning.R
+++ b/R/ArchiveAsyncTuning.R
@@ -170,8 +170,7 @@ ArchiveAsyncTuning = R6Class("ArchiveAsyncTuning",
       # cache benchmark result
       if (self$rush$n_finished_tasks > private$.benchmark_result$n_resample_results) {
         bmrs = map(self$finished_data$resample_result, as_benchmark_result)
-        init = BenchmarkResult$new()
-        private$.benchmark_result = Reduce(function(lhs, rhs) lhs$combine(rhs), bmrs, init = init)
+        private$.benchmark_result = Reduce(function(lhs, rhs) lhs$combine(rhs), bmrs)
       }
       private$.benchmark_result
     }
diff --git a/R/CallbackAsyncTuning.R b/R/CallbackAsyncTuning.R
index 616addad..5de7e246 100644
--- a/R/CallbackAsyncTuning.R
+++ b/R/CallbackAsyncTuning.R
@@ -1,4 +1,4 @@
-#' @title Create Asynchronous Tuning Callback
+#' @title Asynchronous Tuning Callback
 #'
 #' @description
 #' Specialized [bbotk::CallbackAsync] for asynchronous tuning.
@@ -17,6 +17,26 @@ CallbackAsyncTuning = R6Class("CallbackAsyncTuning",
     #' Called in `ObjectiveTuningAsync$eval()`.
     on_eval_after_xs = NULL,
 
+    #' @field on_resample_begin (`function()`)\cr
+    #' Stage called at the beginning of an evaluation.
+    #' Called in `workhorse()` (internal).
+    on_resample_begin = NULL,
+
+    #' @field on_resample_before_train (`function()`)\cr
+    #' Stage called before training the learner.
+    #' Called in `workhorse()` (internal).
+    on_resample_before_train = NULL,
+
+    #' @field on_resample_before_predict (`function()`)\cr
+    #' Stage called before predicting.
+    #' Called in `workhorse()` (internal).
+    on_resample_before_predict = NULL,
+
+    #' @field on_resample_end (`function()`)\cr
+    #' Stage called at the end of an evaluation.
+    #' Called in `workhorse()` (internal).
+    on_resample_end = NULL,
+
     #' @field on_eval_after_resample (`function()`)\cr
     #' Stage called after hyperparameter configurations are evaluated.
     #' Called in `ObjectiveTuningAsync$eval()`.
@@ -52,6 +72,12 @@ CallbackAsyncTuning = R6Class("CallbackAsyncTuning",
 #'            - on_optimizer_before_eval
 #'              Start Evaluation
 #'                - on_eval_after_xs
+#'                  Start Resampling Iteration
+#'                    - on_resample_begin
+#'                    - on_resample_before_train
+#'                    - on_resample_before_predict
+#'                    - on_resample_end
+#'                  End Resampling Iteration
 #'                - on_eval_after_resample
 #'                - on_eval_before_archive
 #'              End Evaluation
@@ -72,7 +98,7 @@ CallbackAsyncTuning = R6Class("CallbackAsyncTuning",
 #' @details
 #' When implementing a callback, each function must have two arguments named `callback` and `context`.
 #' A callback can write data to the state (`$state`), e.g. settings that affect the callback itself.
-#' Tuning callbacks access [ContextAsyncTuning].
+#' Tuning callbacks access [ContextAsyncTuning] and [mlr3::ContextResample].
 #'
 #' @param id (`character(1)`)\cr
 #'  Identifier for the new instance.
@@ -101,6 +127,26 @@ CallbackAsyncTuning = R6Class("CallbackAsyncTuning",
 #'  Called in `ObjectiveTuningAsync$eval()`.
 #'  The functions must have two arguments named `callback` and `context`.
 #'  The argument of `$.eval(xs)` is available in the `context`.
+#' @param on_resample_begin (`function()`)\cr
+#'  Stage called at the beginning of a resampling iteration.
+#'  Called in `workhorse()` (internal).
+#'  See also [mlr3::callback_resample()].
+#'  The functions must have two arguments named `callback` and `context`.
+#' @param on_resample_before_train (`function()`)\cr
+#'  Stage called before training the learner.
+#'  Called in `workhorse()` (internal).
+#'  See also [mlr3::callback_resample()].
+#'  The functions must have two arguments named `callback` and `context`.
+#' @param on_resample_before_predict (`function()`)\cr
+#'  Stage called before predicting.
+#'  Called in `workhorse()` (internal).
+#'  See also [mlr3::callback_resample()].
+#'  The functions must have two arguments named `callback` and `context`.
+#' @param on_resample_end (`function()`)\cr
+#'  Stage called at the end of a resampling iteration.
+#'  Called in `workhorse()` (internal).
+#'  See also [mlr3::callback_resample()].
+#'  The functions must have two arguments named `callback` and `context`.
 #' @param on_eval_after_resample (`function()`)\cr
 #'  Stage called after a hyperparameter configuration is evaluated.
 #'  Called in `ObjectiveTuningAsync$eval()`.
@@ -152,6 +198,10 @@ callback_async_tuning = function(
   on_worker_begin = NULL,
   on_optimizer_before_eval = NULL,
   on_eval_after_xs = NULL,
+  on_resample_begin = NULL,
+  on_resample_before_train = NULL,
+  on_resample_before_predict = NULL,
+  on_resample_end = NULL,
   on_eval_after_resample = NULL,
   on_eval_before_archive = NULL,
   on_optimizer_after_eval = NULL,
@@ -167,6 +217,10 @@ callback_async_tuning = function(
     on_worker_begin,
     on_optimizer_before_eval,
     on_eval_after_xs,
+    on_resample_begin,
+    on_resample_before_train,
+    on_resample_before_predict,
+    on_resample_end,
     on_eval_after_resample,
     on_eval_before_archive,
     on_optimizer_after_eval,
@@ -181,6 +235,10 @@ callback_async_tuning = function(
       "on_worker_begin",
       "on_optimizer_before_eval",
       "on_eval_after_xs",
+      "on_resample_begin",
+      "on_resample_before_train",
+      "on_resample_before_predict",
+      "on_resample_end",
       "on_eval_after_resample",
       "on_eval_before_archive",
       "on_optimizer_after_eval",
diff --git a/R/CallbackBatchTuning.R b/R/CallbackBatchTuning.R
index 2de95b4d..d1c2047b 100644
--- a/R/CallbackBatchTuning.R
+++ b/R/CallbackBatchTuning.R
@@ -24,6 +24,26 @@ CallbackBatchTuning= R6Class("CallbackBatchTuning",
     #' Called in `ObjectiveTuningBatch$eval_many()`.
     on_eval_after_design = NULL,
 
+   #' @field on_resample_begin (`function()`)\cr
+    #' Stage called at the beginning of an evaluation.
+    #' Called in `workhorse()` (internal).
+    on_resample_begin = NULL,
+
+    #' @field on_resample_before_train (`function()`)\cr
+    #' Stage called before training the learner.
+    #' Called in `workhorse()` (internal).
+    on_resample_before_train = NULL,
+
+    #' @field on_resample_before_predict (`function()`)\cr
+    #' Stage called before predicting.
+    #' Called in `workhorse()` (internal).
+    on_resample_before_predict = NULL,
+
+    #' @field on_resample_end (`function()`)\cr
+    #' Stage called at the end of an evaluation.
+    #' Called in `workhorse()` (internal).
+    on_resample_end = NULL,
+
     #' @field on_eval_after_benchmark (`function()`)\cr
     #' Stage called after hyperparameter configurations are evaluated.
     #' Called in `ObjectiveTuningBatch$eval_many()`.
@@ -57,6 +77,12 @@ CallbackBatchTuning= R6Class("CallbackBatchTuning",
 #'          - on_optimizer_before_eval
 #'         Start Evaluation
 #'              - on_eval_after_design
+#'                  Start Resampling Iteration
+#'                    - on_resample_begin
+#'                    - on_resample_before_train
+#'                    - on_resample_before_predict
+#'                    - on_resample_end
+#'                  End Resampling Iteration
 #'              - on_eval_after_benchmark
 #'              - on_eval_before_archive
 #'         End Evaluation
@@ -70,7 +96,7 @@ CallbackBatchTuning= R6Class("CallbackBatchTuning",
 #' ```
 #'
 #' See also the section on parameters for more information on the stages.
-#' A tuning callback works with [ContextBatchTuning].
+#' A tuning callback works with [ContextBatchTuning] and [mlr3::ContextResample].
 #'
 #' @details
 #' When implementing a callback, each function must have two arguments named `callback` and `context`.
@@ -100,6 +126,26 @@ CallbackBatchTuning= R6Class("CallbackBatchTuning",
 #'  The functions must have two arguments named `callback` and `context`.
 #'  The arguments of `$eval_many(xss, resampling)` are available in `context`.
 #'  Additionally, the `design` is available in `context`.
+#' @param on_resample_begin (`function()`)\cr
+#'  Stage called at the beginning of a resampling iteration.
+#'  Called in `workhorse()` (internal).
+#'  See also [mlr3::callback_resample()].
+#'  The functions must have two arguments named `callback` and `context`.
+#' @param on_resample_before_train (`function()`)\cr
+#'  Stage called before training the learner.
+#'  Called in `workhorse()` (internal).
+#'  See also [mlr3::callback_resample()].
+#'  The functions must have two arguments named `callback` and `context`.
+#' @param on_resample_before_predict (`function()`)\cr
+#'  Stage called before predicting.
+#'  Called in `workhorse()` (internal).
+#'  See also [mlr3::callback_resample()].
+#'  The functions must have two arguments named `callback` and `context`.
+#' @param on_resample_end (`function()`)\cr
+#'  Stage called at the end of a resampling iteration.
+#'  Called in `workhorse()` (internal).
+#'  See also [mlr3::callback_resample()].
+#'  The functions must have two arguments named `callback` and `context`.
 #' @param on_eval_after_benchmark (`function()`)\cr
 #'  Stage called after hyperparameter configurations are evaluated.
 #'  Called in `ObjectiveTuningBatch$eval_many()`.
@@ -150,6 +196,10 @@ callback_batch_tuning = function(
   on_optimization_begin = NULL,
   on_optimizer_before_eval = NULL,
   on_eval_after_design = NULL,
+  on_resample_begin = NULL,
+  on_resample_before_train = NULL,
+  on_resample_before_predict = NULL,
+  on_resample_end = NULL,
   on_eval_after_benchmark = NULL,
   on_eval_before_archive = NULL,
   on_optimizer_after_eval = NULL,
@@ -163,6 +213,10 @@ callback_batch_tuning = function(
     on_optimization_begin,
     on_optimizer_before_eval,
     on_eval_after_design,
+    on_resample_begin,
+    on_resample_before_train,
+    on_resample_before_predict,
+    on_resample_end,
     on_eval_after_benchmark,
     on_eval_before_archive,
     on_optimizer_after_eval,
@@ -175,6 +229,10 @@ callback_batch_tuning = function(
       "on_optimization_begin",
       "on_optimizer_before_eval",
       "on_eval_after_design",
+      "on_resample_begin",
+      "on_resample_before_train",
+      "on_resample_before_predict",
+      "on_resample_end",
       "on_eval_after_benchmark",
       "on_eval_before_archive",
       "on_optimizer_after_eval",
diff --git a/R/ObjectiveTuningAsync.R b/R/ObjectiveTuningAsync.R
index 217ef411..6805558b 100644
--- a/R/ObjectiveTuningAsync.R
+++ b/R/ObjectiveTuningAsync.R
@@ -31,7 +31,7 @@ ObjectiveTuningAsync = R6Class("ObjectiveTuningAsync",
       lg$debug("Resampling hyperparameter configuration")
 
       # resample hyperparameter configuration
-      private$.resample_result = resample(self$task, self$learner, self$resampling, store_models = self$store_models, allow_hotstart = TRUE, clone = character(0))
+      private$.resample_result = resample(self$task, self$learner, self$resampling, store_models = self$store_models, allow_hotstart = TRUE, clone = character(0), callbacks = self$callbacks)
       call_back("on_eval_after_resample", self$callbacks, self$context)
 
       lg$debug("Aggregating performance")
diff --git a/R/ObjectiveTuningBatch.R b/R/ObjectiveTuningBatch.R
index 934fddab..3d8ad39a 100644
--- a/R/ObjectiveTuningBatch.R
+++ b/R/ObjectiveTuningBatch.R
@@ -74,7 +74,8 @@ ObjectiveTuningBatch = R6Class("ObjectiveTuningBatch",
       private$.benchmark_result = benchmark(
         design = private$.design,
         store_models = self$store_models,
-        clone = character(0))
+        clone = character(0),
+        callbacks = self$callbacks)
       call_back("on_eval_after_benchmark", self$callbacks, self$context)
 
       # aggregate performance scores
diff --git a/man/CallbackAsyncTuning.Rd b/man/CallbackAsyncTuning.Rd
index 8b36f176..cab809b2 100644
--- a/man/CallbackAsyncTuning.Rd
+++ b/man/CallbackAsyncTuning.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/CallbackAsyncTuning.R
 \name{CallbackAsyncTuning}
 \alias{CallbackAsyncTuning}
-\title{Create Asynchronous Tuning Callback}
+\title{Asynchronous Tuning Callback}
 \description{
 Specialized \link[bbotk:CallbackAsync]{bbotk::CallbackAsync} for asynchronous tuning.
 Callbacks allow to customize the behavior of processes in mlr3tuning.
@@ -20,6 +20,22 @@ For more information on tuning callbacks see \code{\link[=callback_async_tuning]
 Stage called after xs is passed.
 Called in \code{ObjectiveTuningAsync$eval()}.}
 
+\item{\code{on_resample_begin}}{(\verb{function()})\cr
+Stage called at the beginning of an evaluation.
+Called in \code{workhorse()} (internal).}
+
+\item{\code{on_resample_before_train}}{(\verb{function()})\cr
+Stage called before training the learner.
+Called in \code{workhorse()} (internal).}
+
+\item{\code{on_resample_before_predict}}{(\verb{function()})\cr
+Stage called before predicting.
+Called in \code{workhorse()} (internal).}
+
+\item{\code{on_resample_end}}{(\verb{function()})\cr
+Stage called at the end of an evaluation.
+Called in \code{workhorse()} (internal).}
+
 \item{\code{on_eval_after_resample}}{(\verb{function()})\cr
 Stage called after hyperparameter configurations are evaluated.
 Called in \code{ObjectiveTuningAsync$eval()}.}
diff --git a/man/CallbackBatchTuning.Rd b/man/CallbackBatchTuning.Rd
index c09643b8..fa79b4f5 100644
--- a/man/CallbackBatchTuning.Rd
+++ b/man/CallbackBatchTuning.Rd
@@ -28,6 +28,22 @@ callback_batch_tuning("mlr3tuning.backup",
 Stage called after design is created.
 Called in \code{ObjectiveTuningBatch$eval_many()}.}
 
+\item{\code{on_resample_begin}}{(\verb{function()})\cr
+Stage called at the beginning of an evaluation.
+Called in \code{workhorse()} (internal).}
+
+\item{\code{on_resample_before_train}}{(\verb{function()})\cr
+Stage called before training the learner.
+Called in \code{workhorse()} (internal).}
+
+\item{\code{on_resample_before_predict}}{(\verb{function()})\cr
+Stage called before predicting.
+Called in \code{workhorse()} (internal).}
+
+\item{\code{on_resample_end}}{(\verb{function()})\cr
+Stage called at the end of an evaluation.
+Called in \code{workhorse()} (internal).}
+
 \item{\code{on_eval_after_benchmark}}{(\verb{function()})\cr
 Stage called after hyperparameter configurations are evaluated.
 Called in \code{ObjectiveTuningBatch$eval_many()}.}
diff --git a/man/callback_async_tuning.Rd b/man/callback_async_tuning.Rd
index 839688a7..9232a13e 100644
--- a/man/callback_async_tuning.Rd
+++ b/man/callback_async_tuning.Rd
@@ -12,6 +12,10 @@ callback_async_tuning(
   on_worker_begin = NULL,
   on_optimizer_before_eval = NULL,
   on_eval_after_xs = NULL,
+  on_resample_begin = NULL,
+  on_resample_before_train = NULL,
+  on_resample_before_predict = NULL,
+  on_resample_end = NULL,
   on_eval_after_resample = NULL,
   on_eval_before_archive = NULL,
   on_optimizer_after_eval = NULL,
@@ -57,6 +61,30 @@ Called in \code{ObjectiveTuningAsync$eval()}.
 The functions must have two arguments named \code{callback} and \code{context}.
 The argument of \verb{$.eval(xs)} is available in the \code{context}.}
 
+\item{on_resample_begin}{(\verb{function()})\cr
+Stage called at the beginning of a resampling iteration.
+Called in \code{workhorse()} (internal).
+See also \code{\link[mlr3:callback_resample]{mlr3::callback_resample()}}.
+The functions must have two arguments named \code{callback} and \code{context}.}
+
+\item{on_resample_before_train}{(\verb{function()})\cr
+Stage called before training the learner.
+Called in \code{workhorse()} (internal).
+See also \code{\link[mlr3:callback_resample]{mlr3::callback_resample()}}.
+The functions must have two arguments named \code{callback} and \code{context}.}
+
+\item{on_resample_before_predict}{(\verb{function()})\cr
+Stage called before predicting.
+Called in \code{workhorse()} (internal).
+See also \code{\link[mlr3:callback_resample]{mlr3::callback_resample()}}.
+The functions must have two arguments named \code{callback} and \code{context}.}
+
+\item{on_resample_end}{(\verb{function()})\cr
+Stage called at the end of a resampling iteration.
+Called in \code{workhorse()} (internal).
+See also \code{\link[mlr3:callback_resample]{mlr3::callback_resample()}}.
+The functions must have two arguments named \code{callback} and \code{context}.}
+
 \item{on_eval_after_resample}{(\verb{function()})\cr
 Stage called after a hyperparameter configuration is evaluated.
 Called in \code{ObjectiveTuningAsync$eval()}.
@@ -122,6 +150,12 @@ The stages are prefixed with \verb{on_*}.
            - on_optimizer_before_eval
              Start Evaluation
                - on_eval_after_xs
+                 Start Resampling Iteration
+                   - on_resample_begin
+                   - on_resample_before_train
+                   - on_resample_before_predict
+                   - on_resample_end
+                 End Resampling Iteration
                - on_eval_after_resample
                - on_eval_before_archive
              End Evaluation
@@ -142,5 +176,5 @@ A tuning callback works with \link{ContextAsyncTuning}.
 \details{
 When implementing a callback, each function must have two arguments named \code{callback} and \code{context}.
 A callback can write data to the state (\verb{$state}), e.g. settings that affect the callback itself.
-Tuning callbacks access \link{ContextAsyncTuning}.
+Tuning callbacks access \link{ContextAsyncTuning} and \link[mlr3:ContextResample]{mlr3::ContextResample}.
 }
diff --git a/man/callback_batch_tuning.Rd b/man/callback_batch_tuning.Rd
index 4d84e52c..d4ef24ec 100644
--- a/man/callback_batch_tuning.Rd
+++ b/man/callback_batch_tuning.Rd
@@ -11,6 +11,10 @@ callback_batch_tuning(
   on_optimization_begin = NULL,
   on_optimizer_before_eval = NULL,
   on_eval_after_design = NULL,
+  on_resample_begin = NULL,
+  on_resample_before_train = NULL,
+  on_resample_before_predict = NULL,
+  on_resample_end = NULL,
   on_eval_after_benchmark = NULL,
   on_eval_before_archive = NULL,
   on_optimizer_after_eval = NULL,
@@ -50,6 +54,30 @@ The functions must have two arguments named \code{callback} and \code{context}.
 The arguments of \verb{$eval_many(xss, resampling)} are available in \code{context}.
 Additionally, the \code{design} is available in \code{context}.}
 
+\item{on_resample_begin}{(\verb{function()})\cr
+Stage called at the beginning of a resampling iteration.
+Called in \code{workhorse()} (internal).
+See also \code{\link[mlr3:callback_resample]{mlr3::callback_resample()}}.
+The functions must have two arguments named \code{callback} and \code{context}.}
+
+\item{on_resample_before_train}{(\verb{function()})\cr
+Stage called before training the learner.
+Called in \code{workhorse()} (internal).
+See also \code{\link[mlr3:callback_resample]{mlr3::callback_resample()}}.
+The functions must have two arguments named \code{callback} and \code{context}.}
+
+\item{on_resample_before_predict}{(\verb{function()})\cr
+Stage called before predicting.
+Called in \code{workhorse()} (internal).
+See also \code{\link[mlr3:callback_resample]{mlr3::callback_resample()}}.
+The functions must have two arguments named \code{callback} and \code{context}.}
+
+\item{on_resample_end}{(\verb{function()})\cr
+Stage called at the end of a resampling iteration.
+Called in \code{workhorse()} (internal).
+See also \code{\link[mlr3:callback_resample]{mlr3::callback_resample()}}.
+The functions must have two arguments named \code{callback} and \code{context}.}
+
 \item{on_eval_after_benchmark}{(\verb{function()})\cr
 Stage called after hyperparameter configurations are evaluated.
 Called in \code{ObjectiveTuningBatch$eval_many()}.
@@ -111,6 +139,12 @@ The stages are prefixed with \verb{on_*}.
          - on_optimizer_before_eval
         Start Evaluation
              - on_eval_after_design
+                 Start Resampling Iteration
+                   - on_resample_begin
+                   - on_resample_before_train
+                   - on_resample_before_predict
+                   - on_resample_end
+                 End Resampling Iteration
              - on_eval_after_benchmark
              - on_eval_before_archive
         End Evaluation
@@ -124,7 +158,7 @@ End Tuning
 }\if{html}{\out{</div>}}
 
 See also the section on parameters for more information on the stages.
-A tuning callback works with \link{ContextBatchTuning}.
+A tuning callback works with \link{ContextBatchTuning} and \link[mlr3:ContextResample]{mlr3::ContextResample}.
 }
 \details{
 When implementing a callback, each function must have two arguments named \code{callback} and \code{context}.
diff --git a/tests/testthat/test_ArchiveAsyncTuning.R b/tests/testthat/test_ArchiveAsyncTuning.R
index 8246fa53..cdc9465e 100644
--- a/tests/testthat/test_ArchiveAsyncTuning.R
+++ b/tests/testthat/test_ArchiveAsyncTuning.R
@@ -12,6 +12,9 @@ test_that("ArchiveAsyncTuning access methods work", {
     terminator = trm("evals", n_evals = 20),
     store_benchmark_result = TRUE
   )
+
+  expect_benchmark_result(instance$archive$benchmark_result)
+
   tuner = tnr("async_random_search")
   tuner$optimize(instance)
 
diff --git a/tests/testthat/test_CallbackAsyncTuning.R b/tests/testthat/test_CallbackAsyncTuning.R
index ffad721e..6170969e 100644
--- a/tests/testthat/test_CallbackAsyncTuning.R
+++ b/tests/testthat/test_CallbackAsyncTuning.R
@@ -346,3 +346,142 @@ test_that("on_result in TuningInstanceBatchMultiCrit works", {
   expect_equal(unique(instance$result$classif.ce), 0.7)
 })
 
+# stages in mlr3 workhorse -----------------------------------------------------
+
+test_that("on_resample_begin works", {
+  skip_on_cran()
+  skip_if_not_installed("rush")
+  flush_redis()
+
+  callback = callback_async_tuning("test",
+    on_resample_begin = function(callback, context) {
+      # expect_* does not work
+      assert_task(context$task)
+      assert_learner(context$learner)
+      assert_resampling(context$resampling)
+      checkmate::assert_number(context$iteration)
+      checkmate::assert_null(context$pdatas)
+      context$data_extra = list(success = TRUE)
+    }
+  )
+
+  rush::rush_plan(n_workers = 2)
+  instance = tune(
+    tuner = tnr("async_random_search"),
+    task = tsk("pima"),
+    learner = lrn("classif.rpart", minsplit = to_tune(1, 10)),
+    resampling = rsmp ("holdout"),
+    measures = msr("classif.ce"),
+    term_evals = 2,
+    callbacks = callback)
+
+  expect_class(instance$objective$context, "ContextAsyncTuning")
+
+  walk(as.data.table(instance$archive$benchmark_result)$data_extra, function(data_extra) {
+    expect_true(data_extra$success)
+  })
+})
+
+test_that("on_resample_before_train works", {
+  skip_on_cran()
+  skip_if_not_installed("rush")
+  flush_redis()
+
+  callback = callback_async_tuning("test",
+    on_resample_before_train = function(callback, context) {
+      assert_task(context$task)
+      assert_learner(context$learner)
+      assert_resampling(context$resampling)
+      checkmate::assert_number(context$iteration)
+      checkmate::assert_null(context$pdatas)
+      context$data_extra = list(success = TRUE)
+    }
+  )
+
+  rush::rush_plan(n_workers = 2)
+  instance = tune(
+    tuner = tnr("async_random_search"),
+    task = tsk("pima"),
+    learner = lrn("classif.rpart", minsplit = to_tune(1, 10)),
+    resampling = rsmp ("holdout"),
+    measures = msr("classif.ce"),
+    term_evals = 2,
+    callbacks = callback)
+
+  expect_class(instance$objective$context, "ContextAsyncTuning")
+
+  walk(as.data.table(instance$archive$benchmark_result)$data_extra, function(data_extra) {
+    expect_true(data_extra$success)
+  })
+})
+
+test_that("on_resample_before_predict works", {
+  skip_on_cran()
+  skip_if_not_installed("rush")
+  flush_redis()
+
+  callback = callback_async_tuning("test",
+    on_resample_before_predict = function(callback, context) {
+      assert_task(context$task)
+      assert_learner(context$learner)
+      assert_resampling(context$resampling)
+      checkmate::assert_null(context$pdatas)
+      context$data_extra = list(success = TRUE)
+    }
+  )
+
+  rush::rush_plan(n_workers = 2)
+  instance = tune(
+    tuner = tnr("async_random_search"),
+    task = tsk("pima"),
+    learner = lrn("classif.rpart", minsplit = to_tune(1, 10)),
+    resampling = rsmp ("holdout"),
+    measures = msr("classif.ce"),
+    term_evals = 2,
+    callbacks = callback)
+
+  expect_class(instance$objective$context, "ContextAsyncTuning")
+
+  walk(as.data.table(instance$archive$benchmark_result)$data_extra, function(data_extra) {
+    expect_true(data_extra$success)
+  })
+})
+
+test_that("on_resample_end works", {
+  skip_on_cran()
+  skip_if_not_installed("rush")
+  flush_redis()
+
+  callback = callback_async_tuning("test",
+    on_resample_end = function(callback, context) {
+      # expect_* does not work
+      assert_task(context$task)
+      assert_learner(context$learner)
+      assert_resampling(context$resampling)
+      checkmate::assert_number(context$iteration)
+      checkmate::assert_class(context$pdatas$test, "PredictionData")
+      context$learner$state = mlr3misc::insert_named(context$learner$state, list(state_success = TRUE))
+      context$data_extra = list(success = TRUE)
+    }
+  )
+
+  rush::rush_plan(n_workers = 2)
+  instance = tune(
+    tuner = tnr("async_random_search"),
+    task = tsk("pima"),
+    learner = lrn("classif.rpart", minsplit = to_tune(1, 10)),
+    resampling = rsmp ("holdout"),
+    measures = msr("classif.ce"),
+    term_evals = 2,
+    callbacks = callback)
+
+  expect_class(instance$objective$context, "ContextAsyncTuning")
+
+  walk(as.data.table(instance$archive$benchmark_result)$data_extra, function(data_extra) {
+    expect_true(data_extra$success)
+  })
+
+  walk(instance$archive$benchmark_result$score()$learner, function(learner, ...) {
+    expect_true(learner$state$state_success)
+  })
+})
diff --git a/tests/testthat/test_CallbackBatchTuning.R b/tests/testthat/test_CallbackBatchTuning.R
index 750a0943..4ab0fc92 100644
--- a/tests/testthat/test_CallbackBatchTuning.R
+++ b/tests/testthat/test_CallbackBatchTuning.R
@@ -258,4 +258,117 @@ test_that("on_result in TuningInstanceBatchMultiCrit works", {
   expect_equal(unique(instance$result$classif.ce), 0.7)
 })
 
+# stages in mlr3 workhorse -----------------------------------------------------
+
+test_that("on_resample_begin works", {
+
+  callback = callback_batch_tuning("test",
+    on_resample_begin = function(callback, context) {
+      # expect_* does not work
+      assert_task(context$task)
+      assert_learner(context$learner)
+      assert_resampling(context$resampling)
+      checkmate::assert_number(context$iteration)
+      checkmate::assert_null(context$pdatas)
+      context$data_extra = list(success = TRUE)
+    }
+  )
+
+  instance = tune(
+    tuner = tnr("random_search", batch_size = 1),
+    task = tsk("pima"),
+    learner = lrn("classif.rpart", minsplit = to_tune(1, 10)),
+    resampling = rsmp ("holdout"),
+    measures = msrs(c("classif.ce", "classif.acc")),
+    term_evals = 2,
+    callbacks = callback)
+
+   expect_class(instance$objective$context, "ContextBatchTuning")
 
+  walk(as.data.table(instance$archive$benchmark_result)$data_extra, function(data_extra) {
+    expect_true(data_extra$success)
+  })
+})
+
+test_that("on_resample_before_train works", {
+  callback = callback_batch_tuning("test",
+    on_resample_before_train = function(callback, context) {
+      assert_task(context$task)
+      assert_learner(context$learner)
+      assert_resampling(context$resampling)
+      checkmate::assert_number(context$iteration)
+      checkmate::assert_null(context$pdatas)
+      context$data_extra = list(success = TRUE)
+    }
+  )
+
+  instance = tune(
+    tuner = tnr("random_search", batch_size = 1),
+    task = tsk("pima"),
+    learner = lrn("classif.rpart", minsplit = to_tune(1, 10)),
+    resampling = rsmp ("holdout"),
+    measures = msrs(c("classif.ce", "classif.acc")),
+    term_evals = 2,
+    callbacks = callback)
+
+  expect_class(instance$objective$context, "ContextBatchTuning")
+
+  walk(as.data.table(instance$archive$benchmark_result)$data_extra, function(data_extra) {
+    expect_true(data_extra$success)
+  })
+})
+
+test_that("on_resample_before_predict works", {
+  callback = callback_batch_tuning("test",
+    on_resample_before_predict = function(callback, context) {
+      assert_task(context$task)
+      assert_learner(context$learner)
+      assert_resampling(context$resampling)
+      checkmate::assert_null(context$pdatas)
+      context$data_extra = list(success = TRUE)
+    }
+  )
+
+  instance = tune(
+    tuner = tnr("random_search", batch_size = 1),
+    task = tsk("pima"),
+    learner = lrn("classif.rpart", minsplit = to_tune(1, 10)),
+    resampling = rsmp ("holdout"),
+    measures = msrs(c("classif.ce", "classif.acc")),
+    term_evals = 2,
+    callbacks = callback)
+
+  expect_class(instance$objective$context, "ContextBatchTuning")
+
+  walk(as.data.table(instance$archive$benchmark_result)$data_extra, function(data_extra) {
+    expect_true(data_extra$success)
+  })
+})
+
+test_that("on_resample_end works", {
+  callback = callback_batch_tuning("test",
+    on_resample_end = function(callback, context) {
+      assert_task(context$task)
+      assert_learner(context$learner)
+      assert_resampling(context$resampling)
+      checkmate::assert_number(context$iteration)
+      checkmate::assert_class(context$pdatas$test, "PredictionData")
+      context$data_extra = list(success = TRUE)
+    }
+  )
+
+  instance = tune(
+    tuner = tnr("random_search", batch_size = 1),
+    task = tsk("pima"),
+    learner = lrn("classif.rpart", minsplit = to_tune(1, 10)),
+    resampling = rsmp ("holdout"),
+    measures = msrs(c("classif.ce", "classif.acc")),
+    term_evals = 2,
+    callbacks = callback)
+
+  expect_class(instance$objective$context, "ContextBatchTuning")
+
+  walk(as.data.table(instance$archive$benchmark_result)$data_extra, function(data_extra) {
+    expect_true(data_extra$success)
+  })
+})